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检索条件"任意字段=2023 IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2023"

共 4811 条记录，以下是91-100 订阅

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Lp Norm-Based Local Means k-Nearest Neighbor Classification with Feature Reduction

Lp Norm-Based Local Means k-Nearest Neighbor Classification ...

引用

2023 ieee international conference on image processing and computer applications, icipca 2023

作者： Huang, Ziheng Zhang, Yichun Yu, Tianrun School of Computer Science and Technology Southwest University of Science and Technology Mianyang621010 China Maynooth International Engineering College Fuzhou University Fuzhou350108 China School of Information and Mechatronics Engineering Shanghai Normal University Shanghai201418 China

ISBN: (纸本)9798350314670

KNN is an important part of classification algorithms in machine learning, which has been extensively employed in various fields. In this research, we proposed a Lp Norm-Based Local Means k-Nearest Neighbor Classification with Feature Reduction. We extend the original Euclidean distance formula, and obtain the distance calculation formula based on P-value. Then classify and predict the data set after dimension reduction. In the experiment, we verified the effectiveness and superiority of our method by setting different k values in KNN, LMKNN and the classification accuracy obtained by our methodology (PLMKNN). © 2023 ieee.

关键词： Classification (of information)

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Research on Data Science processing System Under computer Virtual Reality Technology

Research on Data Science Processing System Under Computer Vi...

引用

2023 ieee international conference on image processing and computer applications, icipca 2023

作者： Hong, Andrew Liang Sun, Beier An, Ziyuan Shanghai American School Shanghai China San Domenico School San Anselmo United States Sierra Canyon High School CA United States

ISBN: (纸本)9798350314670

This paper designs a digital signal processor based on computer virtual reality technology. The system uses multi-core cooperative work to complete data processing and storage. The system consists of four layers: hardware abstraction layer, system core layer, distributed message communication mechanism and system service layer. Its multi-task kernel implements preemptive scheduling based on priority, synchronization and communication primitives between tasks, real-time interrupt processing and application-oriented cache management mechanism. The system also designed LCD module and keyboard module as man-machine interaction interface. The hardware interface diagram is given. The realization of software programming is also discussed. The experimental results show that multi-core collaborative processing is used to complete data processing and storage functions. The single-core resource utilization rate is effectively reduced by 26%. © 2023 ieee.

关键词： Digital signal processors

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Optical-ISAR image Translation via Denoising Diffusion Implicit Model

Optical-ISAR Image Translation via Denoising Diffusion Impli...

引用

2023 ieee international conference on image processing and computer applications, icipca 2023

作者： Wang, Xinyi Liao, Huaizhang Yang, Zhixiong Han, Tao Xia, Jingyuan College of Electronic Science National University of Defense Technology Changsha China

ISBN: (纸本)9798350314670

Sufficient training data play a crucial role in deep-learning techniques, however, available training samples are not always accessible for specific targets, such as satellites. Producing synthetic data through generative model is a major solution for the lack of training data. In this paper, we propose a Denoising Diffusion Implicit Model (DDIM) to generate the Inverse Synthetic Aperture Radar (ISAR) satellite images from the synthesized optical counterparts. Efficient and abbreviated feature extraction method is proposed in optical-ISAR image translation. A domain-cross generative model is then established with respect to the generative task and feature learning modules obtained from the translation model. Extensive experiments validate the feasibility and performance of the proposed approach, which outperforms the existing generative models such as CycleGAN in terms of inception score and structural similarity index. © 2023 ieee.

关键词： Diffusion

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ResAttUNetPlus: An Attention Network Architecture for Polyp image Segmentation

ResAttUNetPlus: An Attention Network Architecture for Polyp ...

引用

2023 ieee international conference on image processing and computer applications, icipca 2023

作者： Gu, Xinlin Zhang, Luqiao Cyberspace Security Academy Chengdu University of Information Technology Chengdu China

ISBN: (纸本)9798350314670

Intestinal polyps are a prevalent colorectal condition, and early detection and precise identification of lesion areas are crucial to prevent polyp malignancy and enhance treatment outcomes. Colonoscopy is commonly utilized for examining intestinal polyps. In this research, we combine deep learning frameworks from the field of artificial intelligence, specifically computer vision, to perform image segmentation. To develop a more accurate intestinal polyp segmentation model, we propose ResAttUNetPlus, a deep learning network that builds upon ResUNet++ by incorporating improvements in the attention mechanism. The ResAttUNetPlus model comprises three main components: the Encoding layer, Bridge layer, and Decoding layer. Our findings demonstrate that ResAttUNetPlus exhibits a highly effective segmentation performance. When tested on the publicly available dataset Kvasir-SEG using the same hardware facility, the ResAttUNetPlus model achieves more than a one percent improvement in accuracy and mIoU coefficient compared to the ResUNet++ model. This proposed method provides gastroenterologists and patients with a more accurate and efficient technique for image segmentation of intestinal polyps. © 2023 ieee.

关键词： image segmentation

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Research on Multimodal image Fusion Method Based on Deep Learning

Research on Multimodal Image Fusion Method Based on Deep Lea...

引用

2023 ieee international conference on image processing and computer applications, icipca 2023

作者： Jing, Wang Luqiao, Zhang Chengdu University of Information Technology Chengdu617000 China

ISBN: (纸本)9798350314670

Accurate assessment of medical image quality is of paramount importance as it forms the cornerstone of diagnoses, where even minor discrepancies can lead to misdiagnoses. To achieve this, two key aspects, realism, and comprehensibility, are employed to evaluate the quality of medical images. Factors such as image richness, clarity, and saliency are analyzed to precisely reflect the image's reliability. However, due to variations in data originating from different hospitals, medical professionals, and equipment, internal structures may exhibit significant differences. To address this issue, this study proposes a multimodal data-based medical image fusion model that captures the correlation and complementary information between heterogeneous data. By combining the regional Laplacian pyramid (LLP) with convolutional neural networks, the convolutional network is enhanced. The source images are decomposed using LLP, and an improved deep convolutional neural network generates an optimal weight map to guide the fusion process, resulting in a fused image. Experimental results demonstrate that the proposed method achieves favorable fusion effects in both subjective visual assessments and objective evaluation metrics. © 2023 ieee.

关键词： Convolutional neural networks

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Translation processing Model of Complex Long Sentences Based on Feature Extraction Algorithm

Translation Processing Model of Complex Long Sentences Based...

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2023 ieee international conference on image processing and computer applications, icipca 2023

作者： Liu, Jia Wuhan Donghu University Hubei Wuhan430212 China

ISBN: (纸本)9798350314670

With the development of information technology, patents or long sentences of science and technology with huge vocabulary and complex structure have widely appeared in people's daily work. This kind of long sentence often contains several clauses and non-predicate verb phrases, and these clauses and phrases often restrict and depend on each other, thus forming a complex language phenomenon in which there are phrases in clauses and clauses in phrases. This kind of long sentence plays a great role in the logic and rigor of English itself, but it brings considerable difficulties to machine translation. In order to solve this problem, this paper proposes a multilingual and general algorithm for complex long sentence translation based on multi-strategy analysis. The algorithm uses a combination of case-based pattern matching and rule analysis to comprehensively utilize a variety of relevant language features in the source language sentences, including grammatical semantic features, sentence length, punctuation marks Functional words and contextual conditions are used to simplify the segmentation of complex long sentences and compound the translation. © 2023 ieee.

关键词： Pattern matching

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Automated Knee Implant Identification from 2D Templates Using image processing and Artificial Intelligence – An Experimental Approach 1st

Automated Knee Implant Identification from 2D Templates Usin...

引用

1st international conference on Artificial Intelligence and its applications, ICAIA 2023

作者： Jadhav, Renu Purwar, Tanvi Ramanathan, A. Malathy, C. Gayathri, M. Batta, Vineet Department of Networking and Communications School of Computing SRM Institute of Science and Technology KTR Campus Chennai India Department of Computing Technologies School of Computing SRM Institute of Science and Technology KTR Campus Chennai India Department of Orthopedics and Trauma Luton and Dunston University Hospital NHS Trust Luton United Kingdom

ISBN: (纸本)9783031843969

Orthopedic implant identification is the most important step before performing any revision surgery. Automated identification of implants plays a huge role in preoperative planning. All the existing studies on automated identification of implants through artificial intelligence use X-ray images of implants as both training and testing data. The proposed novel and unique work identify the make and model of knee implants from 2D templates and their corresponding preprocessed images in contrast to the use of X-rays in training. The proposed work uses X-ray images of implants only for testing. The experimental approach with different deep learning models provides classification accuracy of 50.83% using raw templates and 55% using preprocessed images and proves to be a vital alternative to the use of X-rays as training data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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Research on the Auxiliary Training System of Tai Chi Fitness Qigong Based on computer 3D image Vision Technology

Research on the Auxiliary Training System of Tai Chi Fitness...

引用

2023 ieee international conference on image processing and computer applications, icipca 2023

作者： Wang, Lin Deng, Weifeng Hainan Vocational University of Science and Technology Haikou China

ISBN: (纸本)9798350314670

Based on Kinect motion capture technology, this paper developed a set of auxiliary teaching software suitable for Tai Chi practice in view of the movement requirements of beginners. This teaching system can show students the standard moves of Tai Chi. A Kinect software system based on motion capture technology is established to realize the standardized measurement of motion capture. The collected data are filtered and de-nosed and stored to form a standard motion database. By collecting the data of the subjects' tai chi movement process, the subjects' movement process is presented in the form of color images in the interface window. The bone data is corresponding to the color image, and the key nodes in the image are identified. After the two sets of data are collected, the Dynamic Time Wide (DTW) method is used to deal with the corresponding frame matching problem. By assigning the rotation information of the connection points to the corresponding Unity3D human body model. Experiments show that the method adopts motion capture technology, DTW and other methods, and has been effectively coordinated with Unity3 3D simulation software for practice and scoring. © 2023 ieee.

关键词： computer software

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CT image Recognition of COVID-19 Based on Improved Resnet Model

CT Image Recognition of COVID-19 Based on Improved Resnet Mo...

引用

2023 ieee international conference on image processing and computer applications, icipca 2023

作者： Jiang, Fuqiang East China University of Science and Technology Shanghai China

ISBN: (纸本)9798350314670

In order to detect COVID-19 positive cases more quickly, in this paper, a deep learning model is used and a CNN network is designed for model training. In addition, some classical networks such as Resnet, VGG and some lightweight networks are tried to train the model and analyze their advantages and disadvantages. Finally, a Resnet network is designed. The Fire module is introduced to reduce the network depth and parameter quantity and make the network pay attention to the global information. The performance of the model on the three loss functions of CrossEntropyLoss, FocalLoss and Poly1FocalLoss is compared, and the optimal loss function is CrossEntropyLoss. The network model has achieved good results in both the training set and the verification set. The accuracy in the training set is 95%, the average accuracy in the verification set is about 80%, and the accuracy in the test set is nearly 75%. Because the model can be trained under the CPU, compared with the 84% accuracy of the original author's DenseNet test set, the precision has slightly decreased, and the recall rate has increased by 4 percentage points. However, in comparison, the speed and accuracy of the improved model have been improved under the Resnet 34 model. © 2023 ieee.

关键词： COVID-19

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Unsupervised Low-Light Enhancement Method Based on Attention Mechanism

Unsupervised Low-Light Enhancement Method Based on Attention...

引用

2023 ieee international conference on image processing and computer applications, icipca 2023

作者： Wu, Yuzhuo Northeast Normal University Department of Computer Science and Information Technology Changchun China

ISBN: (纸本)9798350314670

Enhancing low-light images has been a research topic in the field of image processing. Significant advancements have been made recently using deep learning based approaches in this filed. However, obtaining the paired low/normal-light images in the same scene used for training is difficult, and the datasets lacks real-time, making its application value difficult to be verified. Meanwhile, low-light enhancement methods often ignore the local characteristics of images, which may result in local over or under exposure. To address the problems, the paper proposes a unsupervised generative adversarial network-based approach for image enhancement, incorporating attention in channel and spatial of images to improve the model generalization. Specially, our method does not require paired images as for datasets. At the generator component, we first apply channel attention into each feature map during the downsampling stage to focus on the channel information, and following the upsampling stage, the spatial attention feature map of the input image is multiplied with the generated *** also perform a weighted summation of the input and the generated results to produce more realistic images. In discriminator component, we adopt a global-local discriminator that can enhance local regions while improving global lighting. We validate our method through extensive experiments on various benchmarks, and the results outperform the current cutting-edge methods. © 2023 ieee.

关键词： image enhancement

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